Research Activities

Estimation of the Economic Impact of Multiple Lifeline Disruption:

Memphis Light, Gas and Water Division Case Study

by S.E. Chang, H.A. Seligson, and R.T. Eguchi

NCEER's multi-year, multidisciplinary investigation of the seismic performance of
Memphis Light, Gas and Water (MLGW) Division's lifeline systems culminates in an
assessment of economic impact. This article presents results on the estimated direct and
indirect economic losses that would follow from disruption to electric power, water, and
natural gas service in Memphis/Shelby County, Tennessee in a scenario earthquake occurring
in the New Madrid Seismic Zone. It focuses on methodologies developed to evaluate direct
losses, including lifeline repair costs, revenue losses to the utility itself, and direct
business interruption losses suffered by the utility customers. The evaluation builds upon
results from related NCEER studies, including work by H. Hwang (hazard assessment), M.
Shinozuka and S. Tanaka (systems performance), K. Tierney (business impact assessment), S.
French (GIS), and A. Rose (indirect impact modeling). Further detail will be presented in
a forthcoming NCEER technical report. Questions and comments should be directed to
Stephanie Chang, EQE International, at (714) 833-3303; or email: sec@eqe.com.

Recent disasters such as the Northridge earthquake and the Great Hanshin (Kobe)
earthquake vividly demonstrated the seismic vulnerability of lifeline systems and the
severe socioeconomic impact of lifeline service disruption. Previous NCEER studies have
shown that a magnitude 7.5 New Madrid Seismic Zone (NMSZ) seismic event with an epicenter
at Marked Tree, Arkansas would cause major disruption to many critical lifeline systems in
Memphis/Shelby County, Tennessee (Shinozuka et al., 1992, 1994). The objectives of the
current study were (1) to develop a methodological approach for estimating economic losses
from damage to multiple urban lifeline systems in earthquake disasters, and (2) to
demonstrate its application to Memphis/Shelby County and Memphis Light, Gas and Water's
(MLGW) lifeline systems in the Marked Tree scenario event.

Figure 1 outlines the methodological framework adopted in
this study. Beginning with analysis of the seismic hazard, information on utility lifeline
facilities and applicable vulnerability models can be combined to provide a profile of
utility risk and expected facility damage in the scenario earthquake. This enables
estimation of the repair costs that would be incurred as a result of the physical damage.
Next, expected lifeline service outage and restoration are evaluated based on the damage
patterns. Together with information on utility customers and their usage of lifeline
services, this provides a means for estimating the revenue losses that would be incurred
by the utility company during the service interruption period. To model the direct
economic losses or business interruption impacts, information is needed on the locations
of businesses throughout the affected region and on their economic resiliency to
disruption of various lifeline services. This model enables the estimation of the economic
losses directly caused by lifeline service disruption. The final step consists of applying
an Input-Output (I-O) economic modeling approach to evaluate the additional
"upstream" and "downstream" or indirect economic impacts caused by
this direct business interruption. Results from studies by several NCEER investigators on
the Memphis area provided many of the elements shown in figure 1 (see also Rose et al.,
eds., forthcoming); a primary challenge of this study consisted of synthesizing these
results in order to implement the overall framework.

The study area is shown infigure 2,
together with the expected ground shaking pattern by census tract for the scenario M 7.5
event. Ground shaking is evaluated in terms of Modified Mercalli Intensity (MMI) and is
based on work by H. Hwang and colleagues at the Center for Earthquake Research and
Information at the University of Memphis. Figure
3 depicts the pattern of employment in Shelby County and indicates the geographic
distribution of economic activity in the study area. The geographic information system
(GIS) overlay of information on seismic hazard, lifeline facilities, utility customers,
and economic activity on a census tract basis represented a central means for integrating
the engineering and social science layers of the problem.

The scope of the analysis includes the impact of disruption to water, electric power,
and natural gas systems independently, as well as simultaneously. However, while in this
sense it does evaluate economic interaction, it does not consider functional interaction
between the systems (e.g., between electric power and water systems). Furthermore, while
the entire water delivery system is included in the analysis, for consistency with
previous NCEER studies, the scope is limited to transmission substations and to
distribution system pipelines in the cases of electric power and natural gas systems,
respectively.

The following section provides a summary of the economic impact results of the research
and discusses their significance. Subsequent sections describe the methodologies employed,
data sources, and more detailed results of the analysis.

Summary of Results

The results of the study led to several conclusions regarding the Memphis scenario
event specifically and insights into the expected economic impact of lifeline system
damage more generally:

Economic losses deriving from lifeline damage alone in an earthquake can be
substantial.

The relative significance of repair costs, revenue loss, and economic impact differs
substantially between lifelines. To a large extent, this results from the expectation that
certain lifelines such as electric power would be restored much more quickly than others
following a disaster.

Restoration patterns were found to critically influence the ultimate economic impact
resulting from lifeline damage. While repair costs depend upon damage patterns, both
revenue losses and business interruption losses are linked directly to the duration and
extent of service outage. Restoration estimates for the MLGW lifelines were based on
damage and initial outage estimates produced by M. Shinozuka et al., at Princeton
University and on restoration models based on past California earthquakes with some
adjustments for central U.S. conditions. Figure
4 shows the inferred restoration curves for electric power, water, and natural gas
service for the scenario earthquake. Complete restoration times are estimated at
approximately two weeks for electric power and water and four weeks for natural gas. It is
reassuring that both the shapes and relative positions of the three lifeline curves are
consistent with actual experience in the Northridge and Kobe earthquakes. For example,
electric power is restored most quickly, followed by water and then natural gas.

Direct economic loss results consistent with these outage and restoration patterns for
each of the lifelines individually are shown in table 1.
In this analysis, effects of simultaneous disruption to several
lifelines are not considered. Direct economic loss is estimated in GRP or final demand
terms, rather than gross output terms, to avoid double-counting. The sum of repair costs,
revenue loss, and direct economic loss ranges from $71.5 million for water to $450.9
million (over six times as large) for electricity, with natural gas in the middle range at
$344.7 million.

Not only do the overall impacts differ substantially between the three lifelines, but
so does the relative significance of different types of direct loss. Table 1 shows that
for each of the utilities, revenue losses are relatively minor. In the case of natural
gas, repair costs ($1.5 million) and revenue losses ($5.9 million) are eclipsed by direct
economic or business interruption losses ($337.3 million), which represent 98% of the sum.
Similarly, direct economic losses constitute 90% of the sum of direct impacts for water.
However, in the case of electric power, repair costs ($401.1 million) dominate, while
business interruption losses represent only 11% of the sum. That damage to electric power
facilities entails such high repair costs is consistent with observation in the Northridge
earthquake, for example, where the estimated $137 million damage to electric power
facilities was much higher than the $49 million for water and $60 million for natural gas
facilities (Eguchi 1995). The contrasts in the significance of direct economic or business
interruption loss are closely related to the outage and restoration patterns summarized in
figure 4, where restoration was relatively rapid for electricity and relatively slow for
natural gas.

To the extent possible, it is useful to make order-of-magnitude comparisons between
results in table 1 and estimated losses in the benchmark ATC-25 (1991) study on
"Seismic Vulnerability and Impact of Disruption of Lifelines in the Conterminous
United States." In the scenario NMSZ magntiude 7.0 earthquake used in that study, the
ratio of direct economic loss to repair costs (referred to in ATC-25 as
"indirect" and "direct" losses, respectively) for electric power
transmission systems in the entire impacted area is about 3.0. Results in table 1 indicate
that in the current study, the comparable ratio is about 0.1. This disparity probably
derives in large part from the difference in expected restoration times - two weeks in the
current study and about 14 weeks (for Tennessee) in ATC-25. In view of past earthquake
experience, the shorter time period is expected to better reflect the time to restore
electricity service. Service restoration time may be significantly less than the
time required to complete repairs because in a disaster, emergency measures can be used to
temporarily restore service while repairs are under way. In the case of water, the ratios,
at 2.0 in ATC-25 and 9.5 from table 1, are more comparable. It was not possible to make
the comparison for natural gas because distribution system losses were not evaluated in
ATC-25.

In an actual disaster, the direct economic loss caused by disruption of all three
lifelines is likely to be somewhat greater than for each of the lifelines individually and
somewhat less than their sum. For example, a business that is forced to temporarily shut
down because of electric power loss will not suffer any additional losses caused by
disruption of water service, whereas one that was only forced to scale back production
might. Table 2 shows the results for
multiple lifeline disruption. Direct economic losses are estimated at $350 million in GRP
terms. This leads to repercussions in the economy or indirect loss that amounts to an
additional $70 million. Including repair costs, total direct and indirect loss suffered in
Shelby County as a result of damage to electric power, water, and natural gas lifelines is
estimated at $829 million. This represents about 3% of GRP for Shelby County in the study
year, 1991. Note that revenue losses are excluded in order to avoid double-counting.
Direct and indirect economic impacts comprise 42.2% and 8.4% of the total, respectively.
Thus in this case, direct and indirect economic loss represent roughly the same magnitude
of dollar loss as repair costs.

Methodological Approach

Damage and Repair Cost Assessment

Damage and repair costs were evaluated using results from previous NCEER studies,
existing methodologies, and facility exposure data from MLGW. Probabilistic damage
estimates were provided by M. Shinozuka and S. Tanaka at Princeton University for the
number of pipe breaks or repairs in the water delivery system. Combining this data with
costs per repair, determined from expert consultation, yielded total repair costs. In the
case of electric power, damage and repair cost estimates were made based on ATC-25
methodology and typical substation replacement costs based on data from the Los Angeles
Department of Water and Power (LADWP). For natural gas, pipeline breaks were estimated
using existing models (Eguchi 1991) based on observation in historic earthquakes. Costs
per repair were determined from expert consultation.

Service Outage and Restoration Modeling

Service outage estimates for the scenario earthquake were generally available from
studies by M. Shinozuka et al. In the case of the water delivery system, their results on
probabilistic estimates of initial outage by census tract in Shelby County were utilized
directly. For electric power, similar results were provided by electric power service area
(EPSA) and used to infer outage on a census tract level. Natural gas service was assumed
to be disrupted throughout the county immediately after the disaster, a result that is
consistent with the expected intensity patterns (MMI VIII and VIII ? and experience in
previous earthquakes.

Restoration was modeled based on MLGW lifeline system and service area information and
observations and data from previous earthquakes. For water, estimates of the density of
pipe breaks were used to approximate the amount of time that would be required for work
crews to restore service in a given census tract. Data from the San Fernando earthquake
described in Seligson et al. (1991) provided the means to calibrate the model. Electric
power restoration was estimated by first developing an overall restoration curve
calibrated to the Northridge earthquake and scaled to the scenario event. Expected
restoration times for individual EPSAs and census tracts were derived based on initial
outage and information on customer distribution. For natural gas, total restoration time
was estimated from ATC-25 models. Four recovery "zones" were then delineated
based on knowledge of the different gas pressure systems in Shelby County and an assumed
restoration sequence based on expected damage concentrations.

Revenue Loss Estimation

To estimate expected revenue losses to the utility company, lifeline service disruption
results were combined with information on MLGW's customer base and average daily revenue
per customer by residential, commercial, or industrial class. The geographic location of
MLGW customers throughout the county's census tracts was estimated using census population
data for residential customers. The number of nonresidential customers by census tract was
approximated using unpublished industry employment data that was originally available on a
traffic analysis zone basis. Revenue loss results by user type are shown in table 3. Figures for natural gas pertain to an
average of winter and summer seasons.

Direct Economic Loss Modeling

Direct economic loss models were developed based upon existing methodologies described
in ATC-25; however, several important refinements were made that take advantage of results
from other NCEER studies focusing on Shelby County (see Rose et al., eds., forthcoming).
These refinements include (1) utilization of empirical data on business dependency on
lifelines in Memphis, (2) consideration of duration effects associated with lifeline
outage, (3) evaluation at a geographically disaggregate (i.e., census tract) level, (4)
consideration of multiple lifeline disruption, and (5) evaluation of indirect economic
losses.

The ATC-25 methodology assumes that the first 5% of lifeline service disruption can be
absorbed without economic loss to the user and that subsequent losses would be incurred
proportionally to the extent of disruption up to some maximum level of impact. The maximum
loss associated with complete lifeline outage ("importance" factor) or its
converse ("resiliency" factor) varies according to a particular industry's
dependency on the specific lifeline. Rather than utilize the expert-based factors in
ATC-25, new values were estimated based on results from a study of business vulnerability
to lifeline disruption in Shelby County conducted by K. Tierney at the Disaster Research
Center (DRC) of the University of Delaware. In addition to reflecting business operators'
own assessments, it is expected that this approach also captures differences in utility
usage between the central U.S. and California (for which the factors in ATC-25 were
developed). Further, time-dependent factors were developed to account for the
deterioration of resiliency with the duration of outage.

Table 4 provides a comparison of ATC-25
and Shelby County resiliency factors by major industry and lifeline. For comparability,
the former are averaged over sub-industries and the latter over the first month of outage.
Shelby County factors take into account businesses that do not use natural gas. Resiliency
is generally highest to gas outage and lowest to electricity disruption. Results show that
while the factors are generally consistent for electric power, ATC-25 overestimates
business resiliency to water, and in many industries, natural gas disruption, versus the
Shelby County results. For example, for the finance, insurance and real estate (FIRE)
industry, the electricity resiliency factors are similar (.10 and .07) and indicate low
resiliency to outage. However, ATC-25 factors for water and gas are both .80, whereas the
Shelby County results indicate much lower resiliency (.21 and .51, respectively). With all
else equal, this suggests that previous models of business impact may substantially
underestimate the direct economic losses from lifeline disruption.

Direct economic losses were evaluated on a census tract level by industry, lifeline,
and day following the disaster. For simultaneous disruption of electricity, water and gas,
it was assumed that a particular industry (for the specific day and census tract) would
suffer direct economic loss equal to the greatest of the losses from the three lifelines
individually. This assumption may lead to underestimates of the economic loss from
multiple lifeline disruption.

Indirect Economic Loss Modeling

The indirect impact caused by the direct economic loss was modeled using a methodology
based on Input-Output (I-O) analysis that takes into account inter-industry linkages in
the regional economy. This application of I-O analysis was developed by A. Rose et al. at
Pennsylvania State University and makes two major adjustments to the lifeline disruption
problem: first, model coefficients are adjusted to reflect business resiliency to lifeline
outage; and second, indirect impact is modeled as those losses over and above
direct economic loss that derive from the repercussions of "bottleneck" sector
constraints (Rose et al., forthcoming). In the multiple lifeline disruption case, the
manufacturing, services, and FIRE industries constituted the "bottlenecks" in
the first two weeks, the third week, and the fourth week of disruption, respectively.

Table 5 shows the direct and indirect
economic loss by major industry for the multiple lifeline disruption case. Of the total
($419.6 million), 83% consists of direct economic impact. The results demonstrate that the
significance of indirect losses varies substantially by sector. At one extreme, virtually
all (97%) of the economic losses suffered by the "bottleneck" industries
(manufacturing, FIRE and services) are direct. As expected, indirect losses are much more
sizable for the non-bottleneck industries.

Acknowledgments

The cooperation of MLGW, LADWP, and P. McDonough of Mountain Fuel Supply Co., as well
as efforts by the NCEER investigators mentioned above, are gratefully acknowledged.

Some of the material reported herein is based upon work supported in
whole or in part by the National Science Foundation, the State of New York, the U.S.
Department of Transportation, the Federal Highway Administration, the Federal Emergency
Management Agency and other sponsors. Any opinions, findings, and conclusions or
recommendations expressed in this publication are those of the author(s) and do not
necessarily reflect the views of NCEER or its sponsors.